A Charge-Based Mechanistic Study into the Effects of Process Parameters on Fiber Accumulating Geometry for a Melt Electrohydrodynamic Process

Melt electrohydrodynamic processes, in conjunction with a moveable collector, have promising engineered tissue applications. However, the residual charges within the fibers deteriorate its printing fidelity. To clarify the mechanism through which the residual charges play roles and exclude the confounding effects of collector movement, a stationary printing mode is adopted in which fibers deposit on a stationary collector. Effects of process parameters on generalizable printing outcomes are studied herein. The fiber deposit bears a unique shape signature typified by a central cone surrounded by an outer ring and is characterized by a ratio of its height and base diameter Hdep/Ddep. Results indicate Hdep/Ddep increases with collector temperature and decreases slightly with voltage. Moreover, the steady-state dynamic jet deposition process is recorded and analyzed at different collector temperatures. A charge-based polarization mechanism describing the effect of collector temperature on the fiber accumulating shape is apparent in both initial and steady-state phases of fiber deposition. Therefore, a key outcome of this study is the identification and mechanistic understanding of collector temperature as a tunable process variable that can yield predictable structural outcomes. This may have cross-cutting potential for additive manufacturing process applications such as the melt electrowriting of layered scaffolds.

Supervised Machine Learning for Plants Identification Based on Images of Their Leaves

Botanists study in general the characteristics of leaves to give to each plant a scientific name; such as shape, margin...etc. This paper proposes a comparison of supervised plant identification using different approaches. The identification is done according to three different features extracted from images of leaves: a fine-scale margin feature histogram, a Centroid Contour Distance Curve shape signature and an interior texture feature histogram. First represent each leaf by one feature at a time in, then represent leaves by two features, and each leaf was represented by the three features. After that, the authors classified the obtained vectors using different supervised machine learning techniques; the used techniques are Decision tree, Naïve Bayes, K-nearest neighbour, and neural network. Finally, they evaluated the classification using cross validation. The main goal of this work is studying the influence of representation of leaves' images on the identification of plants, and also studying the use of supervised machine learning algorithm for plant leaves classification.

Classification of Skin Lesion Using (Segmentation) Shape Feature Detection

Malignant melanoma has caused countless deaths in recent years. Many calculation methods have been created for automatic melanoma detection. In this chapter, based on the traditional concept of shape signature and convex hull, an improved boundary description shape signature is developed. The convex defect-based signature (CDBS) proposed in this paper scans contour irregularities and is applied to skin lesion classification in macroscopic images. Border irregularities of skin lesions are the predominant criteria for ABCD (asymmetry, border, color, and diameter) to distinguish between melanoma and nonmelanoma. The performance of the CDBS is compared with popular shape descriptors: shape signature, indentation depth function, invariant elliptic Fourier descriptor (IEFD), and rotation invariant wavelet descriptor (RIWD), where the proposed descriptor shows better results. Multilayer perceptron neural network is used as a classifier in this work. Experimental results show that the proposed approach achieves significant performance with mean accuracy of 90.49%.